Document Type


Original Publication Date


Journal/Book/Conference Title

BMC Systems Biology

DOI of Original Publication



Originally published at

Date of Submission

August 2014


Background Cancer is a complex disease where molecular mechanism remains elusive. A systems approach is needed to integrate diverse biological information for the prognosis and therapy risk assessment using mechanistic approach to understand gene interactions in pathways and networks and functional attributes to unravel the biological behaviour of tumors.

Results We weighted the functional attributes based on various functional properties observed between cancerous and non-cancerous genes reported from literature. This weighing schema was then encoded in a Boolean logic framework to rank differentially expressed genes. We have identified 17 genes to be differentially expressed from a total of 11,173 genes, where ten genes are reported to be down-regulated via epigenetic inactivation and seven genes are up-regulated. Here, we report that the overexpressed genes IRAK1, CHEK1 and BUB1 may play an important role in ovarian cancer. We also show that these 17 genes can be used to form an ovarian cancer signature, to distinguish normal from ovarian cancer subjects and that the set of three genes, CHEK1, AR, and LYN, can be used to classify good and poor prognostic tumors.

Conclusion We provided a workflow using a Boolean logic schema for the identification of differentially expressed genes by integrating diverse biological information. This integrated approach resulted in the identification of genes as potential biomarkers in ovarian cancer.


© 2013 Kumar et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Is Part Of

VCU Biomarker Research and Personalized Medicine Publications

1752-0509-7-12-s1.xls (230 kB)
List of up- and down-regulated genes in the TCGA dataset.

1752-0509-7-12-s2.pdf (52 kB)
Differential/Non-differential gene expression for various functional attributes.

1752-0509-7-12-s3.pdf (180 kB)
Boolean-based probability score for ranking 48 non-differentially expressed genes.

1752-0509-7-12-s4.pdf (152 kB)
Statistically significant pathway analysis from the NCI-naturePID(Pathway Interaction Database) of the 17 differentially expressed genes in various biological pathways.

1752-0509-7-12-s5.pdf (71 kB)
High confidence up/down-regulated genes identified in the Boolean framework with their co-expressed neighbors.

1752-0509-7-12-s6.pdf (1777 kB)
Schematic representation of co-expressed genes with significant Boolean values. Edges are colour-coded to highlight the range of pearson’s correlation coefficient in co-expression network: black (> 0.7), slate grey (0.65 - 0.7), navy blue (0.60 - 0.65), slate blue ( 0.55- 0.60), dark green (0.50 – 0.55), dark olive green (0.45 – 0.50 ). Yellow (0.40 -0.45), indian red (0.35 -0.40 ) and peru (0.30 - 0.35).

1752-0509-7-12-s7.pdf (579 kB)
GATHER [[66]] GO biological process annotations of the 17 differentially expressed genes associated with the cancer hallmarks in Table3.

1752-0509-7-12-s8.pdf (98 kB)
Relevant GO biological process characterization from GeneCards [[67]] for the 17 differentially expressed genes, mapped to cancer hallmarks (HM) in Table 3.

1752-0509-7-12-s9.pdf (177 kB)
Gene expression data for the 17 genes identified in this study across 45 (38 tumor + 7 normal) samples.

1752-0509-7-12-s10.xls (48 kB)
Gene expression data for the 17 genes identified in this study across 45 (38 tumor + 7 normal) samples.